LBP and Color Descriptors for Image Classification

Four novel color Local Binary Pattern (LBP) descriptors are presented in this chapter for scene image and image texture classification with applications to image search and retrieval. Specifically, the first color LBP descriptor, the oRGB-LBP descriptor, is derived by concatenating the LBP features of the component images in an opponent color space — the oRGB color space. The other three color LBP descriptors are obtained by the integration of the oRGB-LBP descriptor with some additional image features: the Color LBP Fusion (CLF) descriptor is constructed by integrating the RGB-LBP, the YCbCr-LBP, the HSV-LBP, the rgb-LBP, as well as the oRGB-LBP descriptor; the Color Grayscale LBP Fusion (CGLF) descriptor is derived by integrating the grayscale-LBP descriptor and the CLF descriptor; and the CGLF+PHOG descriptor is obtained by integrating the Pyramid of Histograms of Orientation Gradients (PHOG) and the CGLF descriptor. Feature extraction applies the Enhanced Fisher Model (EFM) and image classification is based on the nearest neighbor classification rule (EFM-NN). The proposed image descriptors and the feature extraction and classification methods are evaluated using three databases: the MIT scene database, the KTH-TIPS2-b database, and the KTH-TIPS materials database. The experimental results show that (i) the proposed oRGB-LBP descriptor improves image classification performance upon other color LBP descriptors, and (ii) the CLF, the CGLF, and the CGLF+PHOG descriptors further improve upon the oRGB-LBP descriptor for scene image and image texture classification.